Nanoparticle systems (NP) can be used to deliver diagnostics and therapeutics including small and large molecules, gene vectors, and biosensor. As NP is versatile and can be made of different types of materials, and can have different sizes, surface charges, and surface modifications, there is the potential to tailor the design of NP for its intended function. Such goals can be greatly facilitated by quantitative models that predict the NP delivery to target sites and the biointerfaces (e.g., NP disposition and interactions with targets). In general, tumor properties, biological in nature, are dynamic and altered by a variety of variables and can produce diverse and at times unexpected effects on NP disposition. These situations in turn create uncertainties on the fate of NP at target sites and hence questions on the NP design. For example, how should one design NP in anticipation of intratumoral heterogeneity in the transport mechanisms (diffusion vs convection) in different parts of a tumor, or treatment-induced changes in tumor vasculature or properties? What are the margins of error if the NP design/selection does not take into account the diverse/dynamic tumor properties? Similarly, some NP properties by design will produce uncertain or opposite outcomes. For example, NP is frequently surface-modified with targeting ligands, but binding of ligands to cell surface receptors limits NP transport. What are the binding characteristics that would yield an optimal balance between tumor selectivity and tumor penetration? Pegylation increases circulation times but also decreases the endocytosis of NP. What is the range of % pegylation to enable optimal tumor targeting? We propose that the above and similar questions can be addressed by developing computation models that use relatively few in vitro and in vivo experimental data to describe the extravasation, interstitial deposition and transport, and internalization of NP in solid tumors as functions of NP/tumor properties and biointerfaces, and treatment schedules (dose intensity and frequency). We will take a balanced empirical-theoretical approach that uses our combined expertise in pharmacokinetics, drug/NP delivery, modeling, simulations, tumor heterogeneity, and in vitro and in vivo experimentations. The model parameters are either lab-generated, obtained from the literature, calculated using well-known equations, or, in the case of parameters that cannot be measured, by fitting the data to equations. Model performance is evaluated by conducting experiments and comparing the lab-generated data to the model-predicted data. We have developed first-generation models that successfully used in vitro data of drug/NP-cell-protein interactions in 2-D monolayers to predict the in vivo transport/delivery of a small molecule drug and NP to tumors. We further used these models, together with in vivo measurements of vessel density and diameter, to simulate the effect of chemotherapy, as well as the effects of intra-tumoral heterogeneity. This project is expected to contribute to NP design principles and accelerate the development of cancer nanotechnology.